A forecasting model (predictive model, autoregressive model) is a software-implemented model of a system, process, or phenomenon, usable to forecast a value, output, or outcome expected from the system, process, or phenomenon. The system, process, or phenomenon that is modeled is collectively and interchangeably referred to hereinafter as a “process” unless specifically distinguished where used. The value, output, or outcome expected from the process collectively and interchangeably referred to hereinafter as an “outcome” unless specifically distinguished where used.
A variable that affects an outcome of a process is called a factor or a feature. An outcome of a process is dependent upon, affected by, or otherwise influenced by a set of one or more factors. A factor can be independent, to wit, independent of and not affected by other factors participating in a given model. A factor can be dependent upon a combination of one or more other independent or dependent factors.
A forecasting model has to be trained before the model can reliably forecast a future outcome of the process within a specified degree of tolerance. Usually, but not necessarily, the training data includes past or historical outcomes of the process. The training process adjusts a set of one or more parameters of the model.
Time series forecasting uses one or more forecasting models to regress on independent factors to produce a dependent factor. For example, if Tiger Woods has been playing golf very quickly, the speed of play is an example of an independent factor. A forecasting model regresses on historical data to predict the future play rates. The future play rate is a dependent factor.